InfoAgent tracks and fixes facts in editable infographics
InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics
Computer Vision and Pattern Recognition
Summary
Creating accurate infographics is hard because changing one fact or image part means you have to update other connected pieces correctly. The authors built InfoAgent, a tool that keeps track of every fact and its proof as a connected map, so it can generate infographics that stay consistent. When something needs fixing, InfoAgent knows which parts to change and checks that the overall infographic remains correct. This makes fixing errors faster and keeps the design and evidence linked throughout.
What this means in practice
- •For data visualization developers: Create interactive infographic tools that keep data and visuals consistent during updates using dependency-aware repair methods.
- •For business intelligence teams: Generate and update dashboards and reports with traceable evidence, ensuring factual accuracy and easier correction.
Authors
Yifan Li, Tong Li, Qi Zeng, Lishuai Gao, Ruwei Pan, Cong Wei, Shaohua Kevin Zhou, Zhuoliang Kang, Xiaoming Wei
Abstract
Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one element also requires tracking its supporting evidence and the dependencies affected by the change. We present \textbf{InfoAgent}, a training-free framework for \emph{evidence-bound visual-symbolic program synthesis}. Its Infographic Visual Description (IVD) records factual payloads, evidence provenance, execution routes, and verification obligations in a typed dependency graph. Retrieved design priors guide compilation, and layered execution combines raster synthesis with editable symbolic and binding objects while retaining their traces. Dependency-aware repair localizes corrections, rechecks affected dependencies, and requires protected obligations to remain satisfied under the declared checkers. Unresolved obligations remain explicit. On IGenBench, InfoAgent achieves 93.0 Q-ACC and 59.0 I-ACC. We also introduce InfoGraphicBench-Evidence, where complete-checklist pass rates on 200 test requests increase from 21.5\% for Same-IVD Prompt to 23.5\% for the initial layered output and 28.5\% after repair, using the same evidence and initial IVD. On 120 audited repair cases, localized repair edits 12.4\% of the canvas on average, compared with 67.3\% for global regeneration.